AI Blog

AI is a means, not a goal

Melisande Rouger October 2019

Artificial intelligence (AI) software solutions developed for radiology are increasingly focused on answering precise clinical questions, rather than just detecting lesions. This is the most noticeable trend in medical imaging AI, which continues to bring new products to the market with clockwork regularity. Whether it feeds on machine or deep learning, AI in radiology really boils down to one thing:

Noncontrast computer tomography–based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model

European Radiology October 2019

The authors developed a radiomics model for predicting hematoma expansion in patients with intracerebral haemorrhage (ICH) and compared its predictive performance with a conventional radiological feature-based model. Through retrospective analysis and noncontrast computed tomography (NCCT) assessment, it was found that an NCCT-based radiomics model showed better performance in the prediction of early hematoma expansion in ICH patients. Key points Radiomics

Lost track of follow-up recommendations? Maybe AI could help you

Daniel Pinto dos Santos October 2019

By now, everybody knows that AI-based systems achieve impressive results in image analysis. But a recent blog post asked the question of what the next true killer apps could be [1]. Interestingly, the author raised the point that the next killer app may well be outside of the walls of the radiology department (i.e. further away from pixel analysis than

Looking outside the box: the environmental repercussions of AI systems, a review of Stuart Russell’s new book, and teaching machines causality

ESR October 2019

This week in artificial intelligence (AI) news, we take a look at the environmental impact of AI systems, a review of Stuart Russell’s new book on controlling AI, and Yoshua Bengio’s work developing an algorithm to help machines learn cause and effect. When posing questions on the negative consequences that artificial intelligence (AI) may have on society, the economy, and

Radiomics analysis using contrast-enhanced CT for preoperative prediction of occult peritoneal metastasis in advanced gastric cancer

European Radiology October 2019

The authors of this study constructed two multivariate logistic regression models and compared the diagnostic performance between the two of them via receiver operating characteristic analysis, discovering that CT radiomics analysis can provide valuable information for predicting occult peritoneal metastases in advanced gastric cancer. Key points Venous CT radiomics analysis provided valuable information for predicting occult peritoneal metastases in advanced

Embracing Healthcare 4.0: Digitalizing healthcare as a key enabler for high value care

ESR October 2019

The digitalization of healthcare is already underway, yet, because of the slower tempo of technological adoption by healthcare insiders, as compared to other industries, digitalization has not been so obvious. But make no mistake! The digital revolution is coming to a hospital near you, and not a moment too soon! Four areas of digitalization will become increasingly familiar to healthcare

Developing a code of ethics on AI in radiology: a multisociety statement

Raymond Geis October 2019

An international statement on the topic of ethics of artificial intelligence (AI) in radiology has been written by a group consisting of the following societies: the American College of Radiology (ACR), the European Society of Radiology (ESR), the Radiology Society of North America (RSNA), the Society for Imaging Informatics in Medicine (SIIM), the European Society of Medical Imaging Informatics (EuSoMII),

Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation

Richard Do September 2019

The goal of this study was to measure the reproducibility of radiomic features in pancreatic parenchyma and ductal adenocarcinomas in patients who underwent consecutive contrast-enhanced computed tomography (CECT) scans. A majority of features were not reproducible (as defined by a concordance correlation coefficient of greater than 0.9) when comparing their values across consecutive CECTs obtained within a two week period.

Looking outside the box: AI surveillance systems, restaurants going futuristic, and AI at the 2020 Olympic Games

ESR September 2019

This week in artificial intelligence (AI) news, we take a look at the growing use of surveillance systems utilizing AI technologies, the use of robotics and algorithms in the restaurant industry, and innovation at the 32nd Summer Olympic Games in Tokyo, Japan. In true 1984 fashion, many countries around the globe, both democracies and autocracies, are adopting the use of

Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules

European Radiology September 2019

This article sets out to determine whether machine learning can be used to train and calibrate the signature for diagnosing hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. The authors proved that artificial intelligence could enhance clinicians’ decision and reduce the rate of cirrhotic patients requiring liver biopsy. Key points In cirrhotic patients with visually indeterminate liver nodules, expert

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